Integrating Process Simulation and Life Cycle Assessment for Enhanced Process Efficiency and Reduced Environmental Impact in Ferromanganese Production

Abstract

Process simulation was integrated with life cycle assessment to evaluate process efficiency and environmental impact of the production of manganese ferroalloys for various production modes and different ore mineralogies. Utilizing HSC Sim, the model was designed to evaluate the production process with or without a pretreatment step, where results for simulated cases using a four-zone ferromanganese furnace model were exported to openLCA for a complete life cycle assessment. Two main production scenarios were simulated: a closed ferromanganese furnace running on the duplex method and an open furnace running on the discard slag approach. The closed furnace scenario achieved a 17.5% reduction in energy consumption and a 16.2% decrease in direct CO₂ emissions with CO-rich off-gas pretreatment. The open furnace scenario showed an 11.2% reduction in energy consumption with thermal solar energy pretreatment but no change in CO₂ emissions.

1. Introduction

Manganese ferroalloys, such as ferromanganese and silicomanganese, play a critical role in the production of high-quality steels. Used as an additive in the steelmaking process, these alloys function as a deoxidizer and desulphurizer, as well as enhance desirable properties in the steel, including strength and hardness. The production of manganese ferroalloys commonly takes place by carbothermic reduction in submerged arc furnaces, where manganese ores, metallurgical coke, and potential fluxes are the main raw materials. As the process is energy-intensive and leads to emissions of carbon dioxide (CO₂) and other greenhouse gases, numerous efforts are being made to decrease the environmental footprint of the production process. One such effort is the H2020 project PreMa (2018–2023) [1], which aimed to improve energy efficiency and reduce CO₂ emissions by introducing a pretreatment step, thus dividing the current furnace process into two separate units. Due to the diverse raw materials, operational approaches, and furnace technologies employed across various ferromanganese smelters, the development of a universally applicable “plug-and-play” pretreatment unit is not feasible. Rather, the pretreatment technology and strategy must be tailored to the specific needs and conditions of each individual smelter. One approach may involve the use of gas, such as byproduct gases from the submerged arc furnace that contain a substantial amount of carbon monoxide. Alternatively, hydrogen can be used, as it has proven to be an effective reducing agent for manganese ores [2]. Additionally, solar thermal energy presents a promising alternative. In regions like South Africa, where solar energy is abundant, solar thermal applications in mineral processing hold significant potential for reducing dependence on fossil fuels [3], including manganese ores [4]. To address this complexity of ferromanganese production, creating representative simulation models becomes instrumental, as this offers the capability to quickly assess numerous scenarios. This allows for the exploration of a wide range of process parameters and technologies, facilitating efficient identification of optimal solutions and ultimately accelerating the decision-making process. Furthermore, it will enhance the overall understanding of the production process, leaving a valuable tool for continuous improvement and sustainable development. The literature indicates that simulation models are a common method for studying industrial processes. For manganese ferroalloy production, dedicated studies focus on modeling and simulating specific aspects of the production process. Research has addressed the low-temperature behavior of manganese ore prereduction, with models developed based on both isothermal [5] and non-isothermal experimental studies [6]. Furthermore, several publications have investigated the high-temperature dynamics of manganese furnaces, including metal-producing reactions [7] and slag dripping in the coke bed zone [8]. It is also relatively common to construct mass- and energy balance type models to describe, e.g., laboratory scale experiments or pilot scale experiments, and these may include a varying number of adjustable parameters. For example, Mukono [9] utilized mass and energy balances to assess the prereduction degree obtained in pilot scale experiments.

While simulation models are excellent for insights into operational efficiency and performance, a comprehensive understanding of the environmental impact of a system requires the use of life cycle assessments (LCA). LCA is a standardized method detailed in ISO standards such as ISO 14040:2006 [10] and ISO 14044:2006 [11], as well as in standards addressing specific applications like the carbon footprint of products (ISO 14067:2018) [12]. LCA may provide a holistic perspective on the environmental impact of metallurgical processes. Previous LCA studies on manganese ferroalloy production include work by Haque and Norgate [13], who estimated greenhouse gas emissions in Australia, and Westfall et al. [14], who presented cradle-to-grave potential environmental impacts and indicators for HC FeMn, SiMn, and a theoretical “average manganese alloy” using data from 16 mines and smelters. Conducting an LCA by collecting production data from ferroalloy producers provides real-world insights and an accurate representation of operational practices. However, this approach may be limited by data availability or variability in practices among different producers. In contrast, using a simulation model allows for greater flexibility and control in exploring various scenarios, thus enhancing the ability to assess hypothetical changes and new process developments. Simulation-based approaches for conducting life cycle assessments have gained increased attention in metallurgical industries over recent years [15,16,17,18]. This approach guarantees consistent comparisons across various scenarios and facilitates a precise definition of the production process. It also allows for the incorporation of empirical data into the evaluation, enhancing the accuracy and reliability of the assessments.

This paper details the development of a simulation model for the production of manganese ferroalloys, initiated as a part of the PreMa project. The simulation model, developed using HSC Sim, is designed to efficiently and effectively evaluate various pretreatment scenarios across different charge mixtures and operational modes. It assesses the ferromanganese production process both with and without the pretreatment step, allowing investigation of potential energy sources such as CO-rich off-gas, biocarbon, and thermal solar energy. The model is constructed using existing knowledge and available literature on process relationships, incorporating insights from both industrial practices and smaller-scale experimental setups. Two different charge mixtures from two different example smelters are presented together with suitable pretreatment strategies for these. Finally, the complete life cycle assessment on the basis of the developed simulation model is presented.

2. Methods

2.1. Production Process Description

The production of manganese ferroalloys involves feeding a charge mixture of typically manganese sources, reductants, and fluxes to the top of the furnace. As these materials descend through the furnace, they are exposed to increasing temperatures. Ultimately, slag and alloy are tapped from the lower section, while a process gas rich in CO and CO₂ ascends the furnace and emerges from the charged surface. For closed furnaces, this off-gas is collected and thus available for further utilization. The off-gas may have a CO₂/(CO+CO₂) ratio of 0.23–0.37 [19], implying there are considerable amounts of chemical energy present, where the exact ratio depends on the raw materials used and the production mode. The production primarily utilizes either the discard slag method or the more prevalent duplex method. In the discard slag method, the ores are reduced into the alloy in a single stage, typically achieving 80% Mn recovery, while around 15–20% of MnO remains in the slag. In contrast, the duplex method yields a tapped slag with a higher MnO content (ranging from 30–50%), and this slag serves as a raw material in the production of silicomanganese. The final slag from the silicomanganese process may contain as little as 5% MnO, resulting in an overall manganese recovery rate of 85–90% [20].

The furnace interior is typically divided into two reaction zones: the upper section, characterized by lower temperatures, is known as the prereduction zone, whereas the lower high-temperature region is referred to as the coke bed zone. An illustration of the furnace including the chemical reactions occurring as materials descend the furnace is shown in (Figure 1). As the raw materials descend in the prereduction zone, they will be heated through convection from the hot gases ascending from the coke bed zone, but also through conduction and radiation heat. Increasing temperatures lead to the initiation of exothermic gas–solid reactions that will reduce higher manganese oxides in the ores to MnO. Iron oxides, which are always present to some extent in the ore, will be reduced in a similar manner, where a complete reduction to metallic iron is possible. As manganese ores vary significantly in both chemical and physical characteristics, the process relations may vary significantly depending on which manganese materials are used for production. Some are oxides, others are carbonates, and they exhibit varying iron concentrations and moisture contents, as well as significant differences in strength and porosity. At sufficient depth in the furnace, the temperatures are high enough that the materials (except coke) form a slag phase. This high-temperature region on the lower parts of the furnace is referred to as the coke bed zone, and it consists of a solid coke bed, liquid slag, and liquid alloy. The liquid slag contains MnO and SiO₂, in addition to Al₂O₃, CaO, and MgO. These oxides are usually present in the ores, and the concentration of the two latter is often increased by the addition of fluxes. MnO and some SiO₂ will be reduced by carbon to a metallic state from the slag phase, which, together with iron and some dissolved carbon (typically 7% for high carbon ferromanganese), forms the tapped alloy.

The production of manganese ferroalloys requires a carbon reductant to reduce MnO and SiO₂ from the slag phase, where the reaction extents will be dependent on temperature and slag chemistry. Additionally, carbon is dissolved in the alloy until saturation, typically 7% in high carbon ferromanganese (HC FeMn). The third factor contributing to the carbon consumption in the process, leading to excessive coke/carbon consumption, is non-ideal behavior in the prereduction zone. The Boudouard reaction (C + CO₂ = 2 CO) is initiated at temperatures exceeding 800 °C in a ferromanganese furnace, where solid carbon reacts with any present CO₂ (a byproduct of ore prereduction) in an endothermic process. To achieve optimal energy and coke consumption, it is thus crucial that the prereduction of the ore is completed at temperatures below 800 °C. This, however, is not the case in industrial production, and it has, as an example, been reported that only 10–40% of the final reduction step for manganese oxide in the prereduction zone (i.e., Mn₃O₄ to MnO) has occurred at 800 °C [21]. Given the substantial impact of prereduction, numerous investigations have been conducted to comprehensively understand the behavior of manganese ores. Research has revealed significant disparities in reduction rates among commercial manganese ores, emphasizing that the rates are driven by kinetics and highly dependent on the ore characteristics. Hence, optimization of prereduction behavior in the submerged arc furnace or design of a pretreatment unit for manganese ores requires an understanding of the factors affecting the reaction rates for the ores used in production.

2.2. Model Architecture

The simulation model was developed as a flow sheet mass and energy balance type model in HSC Sim. The model is shown in Figure 2. The model consists of two main reaction units: the pretreatment unit and the submerged arc furnace. Simulations can be conducted for a stand-alone submerged arc furnace (mirroring the current practice), or by including pretreatment of selected raw materials before the final reduction in the submerged arc furnace. The submerged arc furnace was divided into four temperature zones, according to the definitions presented by Olsen et al. [22], where zone 1 is the drying and calcination zone, zone 2 is the gas reduction zone, zone 3 is the direct reduction zone (resulting from the active Boudouard reaction), and zone 4 is the smelting reduction zone (coke bed zone). All reactions occurring from raw materials to the product were defined in the model in dedicated reaction input tables that were displayed in the interface, as shown in

Table 1. Reaction extents for reactions occurring in the submerged arc furnace used in the HSC Sim model for ferromanganese production. Reaction extent marked “x” is calculated by the model.

		Zone 1	Zone 2	Zone 3	Zone 4
		25–600 °C	600–800 °C	800–1250 °C	1250–1420 °C
General	H2O = H2O(g)	100	100	100	100
	H2O(g) + CO(g) = H2(g) + CO2(g)	10	100	100	100
	2 FeO∙OH = Fe2O3 + H2O(g)	100	100	100	100
	3Fe2O3 + CO = 2Fe3O4 + CO2
	Fe3O4 + CO = 3 FeO + CO2
	FeO + CO = Fe + CO2	0	0	50	100
	CaMg(CO3)2 = CaCO3 + MgO + CO2	100	100	100	100
	CaCO3 = CaO + CO2	0	0	100	100
	MgCO3 = MgO + CO2	100	100	100	100
	MnO + C = Mn + CO	0	0	0	x
	SiO2 + 2C = Si + 2 CO	0	0	0	x
	C = C	0	0	0	x
Comilog	2 MnO2 + CO = Mn2O3 + CO2	100	100	100	100
	3 Mn2O3 + CO = 2 Mn3O4 + CO2	100	100	100	100
	Mn3O4 + CO = 3 MnO + CO	42.9	76.4	100	100
Nchwaning	3 Mn2O3 + CO = 2 Mn3O4 + CO2	4	100	100	100
	Mn3O4 + CO = 3 MnO + CO	0	14.6	100	100
Kudumane	3 Mn2O3 + CO = 2 Mn3O4 + CO2	4	100	100	100
	Mn3O4 + CO = 3 MnO + CO	0	14.6	100	100
UMK	3 Mn2O3 + CO = 2 Mn3O4 + CO2	4	100	100	100
	Mn3O4 + CO = 3 MnO + CO	0	14.6	100	100

. Certain reaction extents will be constant, as they do not depend on the raw materials, e.g., evaporation of moisture will always occur 100% in zone 1 (25–600 °C). For other reactions that are dependent on the specific raw material mixture used, the extent in a given zone will need to be based on empirical data found in the literature. Two main aspects required assumptions through empirical relations: the distribution of manganese between alloy and slag (Mn recovery) and the prereduction behavior of the manganese ores. For the former, a relation presented by Olsen and Tangstad [23] that reflects the relation between the manganese content of the slag and the slag basicity was used. This formula predicts the MnO content in the slag and, thus, the Mn-content in the alloy. To describe the manganese ore prereduction accurately, it is essential to account for variations in the chemical and physical properties of the ores. These differences in composition/mineralogy and physical characteristics lead to distinct reaction rates and extents for each ore type. For example, Comilog predominantly consists of MnO₂ oxides, which will undergo a stepwise reduction sequence from MnO₂ to Mn₂O₃, Mn₃O₄, and finally, MnO. In contrast, Nchwaning ore lacks MnO₂, thus bypassing the initial reduction step observed in Comilog. Although both ores will eventually contain Mn₂O₃ at a certain temperature, the reduction rates of Mn₂O₃ will differ due to their distinct physical characteristics. To describe the behavior in a representative manner, experimental data from small-scale laboratory investigations were used. These experiments involved heating 11–15 mm particles at 6 °C/min in 50% CO—50% CO₂ atmosphere within the temperature range of 25–1000 °C. The resulting reaction extent, determined from the TGA-curves [6,24,25], was used as input to construct reaction extent tables. Taking Comilog ore as an example (Table 1), when the ore exits zone 2 (at 600 °C), all MnO₂ has transformed into Mn₂O₃, Mn₂O₃ has completely reduced to Mn₃O₄, and 42.9% of Mn₃O₄ has further reduced to MnO. As the ore enters zone 2, a combination of Mn₃O₄ and MnO oxides is present. In contrast, Nchwaning ore is in the early stages of prereduction at this temperature, with only 4% of Mn₂O₃ reduced to Mn₃O₄. Reaction extents for iron oxides were equal to those of manganese oxides. For Kudumane and UMK ore, it has previously been observed that the manganese and iron oxides behave relatively similarly as in Nchwaning ore, so the same reaction extents were applied. These estimations of reaction extents from laboratory scale prereduction behavior showed a strong correlation with prereduction behavior seen in pilot scale [26] and industrial scale [19,21] and thus were deemed representative.

Incorporating pretreatment requires defining reaction extents for the unit in a similar manner as for the submerged arc furnace. If the pretreatment unit operates in a reducing atmosphere, the same experimental data used to determine reaction extents for the submerged arc furnace will be utilized. If manganese ores are heated in a non-reducing atmosphere, which may be a potential form of pretreatment, the conversion of the manganese oxides will be more or less in line with that predicted by thermodynamics [24]. Literature shows that MnO₂ will be converted to Mn₂O₃ between 600 °C to 800 °C when heated non-isothermally, whereas a continued conversion to Mn₃O₄ will require temperatures above 981 °C [22].

The HSC Sim software v.10 (https://mogroup.com/hsc) has an integrated extension that enables the data to be exported directly into a life cycle assessment software. In this case, the simulation results were exported into OpenLCA v.1 (https://openlca.org), where the Ecoinvent database was used. This provides analysis for a range of impact categories, including climate change (GWP100), freshwater eutrophication (FEP), photochemical oxidant formation (POFF), terrestrial acidification (TAP100), and water depletion (WDP). The current study focuses on climate change (GWP100).

2.3. Cases

Two main production scenarios were simulated where one illustrates a closed ferromanganese furnace operating on the duplex method, and the other is an open furnace running on discard slag approach. The two scenarios were chosen to illustrate the wide range of production modes utilized in the ferromanganese industry. For each of the two scenarios, the model was used to determine the optimal approach for pretreatment. The optimal case will be presented, giving a total of four cases presented in this paper. The charge mixtures were determined from typical raw materials that may be used in the two production modes. The chemical composition of raw materials used in the simulations is shown in

Table 2. Chemical composition and selected characteristics of some commercial manganese ores.

	Comilog	Nchwaning	UMK	Kudumane	Quartz	Metallurgical Coke
C						88.0
MnO2	76.8
Mn2O3	1.6	62.3	30.7	28.9
Mn3O4		3.0	21.6	24.1
MnO	2.7
CaMg(CO3)2			17.8	17.8
SiO2	3.3	3.9	5.4	5.4	94.3	5.1
Al2O3	5.8	0.4	0.5	0.4	2.1	3.3
Fe2O3		14.9	6.7	6.4
FeO∙OH	4.4				1.5
BaO	0.2
P	0.1
K2O	0.9
CaO	0.6	2.0			1.9
CaCO3		7.9	11.7	11.7
MgO	0.2		2.9	2.2
MgCO3		2.9
H2O(bound moisture) *	4.6	0.0	0.0	0.0

* estimated by thermogravimetry.

. To determine the content of specific minerals, chemical analysis was used in combination with mineralogy studies.

For the duplex operation, a mixture of 50% Comilog ore and 50% Nchwaning ore was used. The amount of free moisture that can be retained in the ores depends on their porosity and may vary throughout the year depending on storage facilities and rainfalls. In the current simulations it was assumed that a 10% moisture content in Comilog and 5% in Nchwaning. The feed rates were scaled to a 40 MW furnace, assuming 100% operation time and no losses. For the discard slag practice, a mixture of Kudumane and UMK manganese ores was used. A surface moisture content of 1.5% was assumed for both ores. In addition, quartz was added as a fluxing agent to adjust the slag basicity. The feed rates were scaled to a 28 MW furnace, assuming 100% operation time and no losses. For cases with pretreatment, a 100 °C temperature decrease in manganese ores during the transfer from the pretreatment unit to SAF was assumed. Furthermore, it was assumed a 7% carbon content in the produced alloy for all cases.

3. Results and Discussion

3.1. Manganese Ferroalloy Production in Closed Furnace

Table 3. Charge mixture and composition of produced alloy, slag and off-gas for stand-alone 40 MW SAF production for a charge mixture of 50% Comilog ore—50% Nchwaning ore. LB1 = (CaO + MgO)/SiO₂.

Input		Output
		Alloy (20.2 t/h)		Slag (9.4 t/h)		Off-Gas (12,761 Nm3/h)
	t/h		%		%		Mol%
Comilog ore	20	Mn	79.4	MnO	46.8	CO	30.9
Nchwaning ore	20	Fe	13.1	CaO	14.9	CO2	53.9
Coke	7.6	Si	0.2	MgO	3.3	H2 + H2O	15.2
Moisture	2.3	C	7.0	Al2O3	15.1
				SiO2	17.7
				LB1	1.0
		Mn/Fe	6.1	Al2O3/SiO2	0.86	CO2/(CO + CO2)	0.64

shows the input charge mixture and the output composition of the produced alloy, slag, and off-gas for a 40 MW furnace producing high carbon ferromanganese alloy from Comilog ore and Nchwaning ore. An alloy containing approximately 80% of manganese with a Mn/Fe-ratio of 6.1 is produced. The slag-to-alloy ratio is 0.47, and an off-gas containing 31% of CO is produced.

When operating a closed furnace, pretreatment that utilizes the off-gas from the submerged arc furnace can be installed. The gas can either be introduced directly to the pretreatment unit, where the CO will then function as a reductant, or the CO can be burned to CO₂, releasing energy to use the off-gas purely as a heating medium. However, on the ton CO₂ per alloy basis, prereduction is more advantageous than heating in inert atmospheres (like CO₂), as it minimally affects the oxygen levels in the manganese ore. If used as a reductant, the CO will reduce the manganese ores in an exothermic manner, where the final reduction extent depends on the maximum temperature obtained, as well as the holding time. Due to the strong exothermic nature of the manganese ore reduction, it was found that feeding the off-gas directly from the submerged arc furnace to the pretreatment unit can, in this case, lead to a complete reduction of the higher manganese oxides in the ores to MnO. A temperature of 900 °C was achievable without any addition of external heat or energy due to the strong exothermic nature of the reduction reactions. An illustration of the flows in the evaluated system with pretreatment is shown in Figure 3. The manganese ores are fed to the pretreatment unit, where they meet the off-gas from the submerged arc furnace, reducing higher manganese oxides to MnO. The pretreated ore is mixed with the reductant before entering the submerged arc furnace.

Table 4. Off-gas from pretreatment unit and submerged arc furnace for production of high carbon ferromanganese using an ore blend of Comilog and Nchwaning ore.

	Submerged Arc Furnace	Pretreatment Unit
CO, mol%	100	9.9
CO2, mol%	0	50.4
CO2/(CO + CO2)	-	0.84
Temperature [°C]	800.7	488.5

shows the off-gas from the submerged arc furnace and the pretreatment unit when running the pretreatment unit on the off-gas from the submerged arc furnace at a temperature of 900 °C. Note that the introduction of a pretreatment unit to the process line will not affect the composition of the alloy and slag. When the higher manganese oxides are fully converted to MnO in the pretreatment unit, the off-gas from the submerged arc furnace will consequently be 100% CO. This is valid in the model due to the simplification of excluding volatiles and moisture from the composition of metallurgical coke. In reality, however, the reductants will contain a certain amount of moisture and other volatile substances that may exit with the off-gas if the reductant is fed directly to the submerged arc furnace.

The energy consumption in the stand-alone submerged arc furnace production is calculated to be 1803 kWh/tonne alloy (

Table 5. Comparison of energy and carbon consumption for production of high carbon ferromanganese with and without pretreatment for an ore blend of Comilog and Nchwaning ore in a closed furnace.

	Stand-Alone SAF	SAF + Pretreatment	% Change
(1) Energy consumption [kWh/tonne alloy]	1803
(2) Unutilized energy [kWh/tonne alloy]	944
(1) + (2): Total energy balance [kWh/tonne alloy]	2748	2268	−17.5
Coke consumption [kg/tonne alloy]	357	310	−13.2
Direct CO2 emissions [tonne CO2/tonne alloy]	0.99	0.83	−16.2

). It is further seen that the process leads to 944 kWh/tonne alloy unutilized energy exiting the system through the off-gas. In total, this gives an energy balance of 2748 kWh/tonne alloy. Routing the off-gas into a pretreatment unit will decrease the overall energy consumption to 2268 kWh/tonne alloy, which correlates to a reduction of 17.5%. The carbon consumption will be reduced from 357 kg C/tonne alloy to 310 kg C/tonne alloy, correlating to a reduction of 13.2%. This is due to a minimization of the Boudouard reaction in the submerged arc furnace. The direct CO₂ emissions from the process decreased by 16.2%.

3.2. Manganese Ferroalloy Production in Open Furnace

Table 6. Charge mixture and composition of produced alloy, slag, and off-gas for stand-alone 28 MW SAF production for a charge mixture of 39% Kudumane ore and 61% UMK ore. LB1 = (CaO + MgO)/SiO₂.

Input		Output
		Alloy (9.2 t/h)		Slag (11.9 t/h)		Off-Gas (7673 Nm3/h)
	t/h		%		%		Mol%
Kudumane ore	9.4	Mn	80.0	MnO	20.1	CO	66.7
UMK ore	14.9	Fe	12.7	CaO	25.6	CO2	25.2
Quartz	3.1	Si	0.2	MgO	13.7	H2	0.8
Coke		C	7.0	Al2O3	2.6	H2O	7.3
Moisture				SiO2	38.1
				LB1	1.03
		Mn/Fe	6.3	Al2O3/SiO2	0.07	CO2/(CO + CO2)	0.3

shows the input charge mixture and the output composition of the produced alloy, slag, and off-gas for a 28 MW furnace producing high carbon ferromanganese alloy from UMK- and Kudumane ore mixture with quartz as a fluxing agent. The furnace being open implies that this smelter does not have the possibility to utilize the off-gas from the SAF in a potential pretreatment. Hence, other heating mediums or energy sources will need to be used. When manganese ores are heated in a non-reducing atmosphere, a gas will be generated from the evaporation of moisture and decomposition of manganese oxides and/or carbonates. In the current study, it was investigated to use this generated gas as the heating medium in the pretreatment unit by circulating the gas through a heat exchanger. The flow sheet for this production setup is shown in Figure 4. UMK ore, Kudumane ore and quartz are fed through the pretreatment unit. The pretreatment unit operates at a temperature of 600 °C. This implies that moisture in the raw materials will evaporate, and carbonates in the form of MgCO₃ and CaMg(CO₃)₂ will decompose. These reactions result in a gas containing 47% CO₂ and 53% H₂O. This gas is routed to a heat exchanger, where it is heated from 300 °C to 900 °C before being routed to the pretreatment unit again. The pretreated raw materials are mixed with reductant before the mixture is fed to the submerged arc furnace.

Table 7. Comparison of energy and carbon consumption for production of high carbon ferromanganese with and without pretreatment for a ore blend of Kudumane and UMK ore in an open furnace.

	Stand-Alone SAF	SAF + Pretreatment	% Change
Energy consumption [kWh/tonne alloy]	3040	2700	−11.2
Energy demand heat exchanger [kWh/tonne alloy]		535
Coke consumption [kg C/tonne alloy]	422	422	0
Direct CO2 emissions [tonne/tonne alloy]	1.44	1.44	0
* Indirect CO2-emissions [tonne CO2/tonne alloy]	2.88	2.56	−11.2

* CO₂-emissions related to electricity usage.

shows the energy consumption, coke consumption and CO₂ emissions for the stand-alone submerged arc furnace production and the process where pretreatment is included. The energy consumption of the submerged arc furnace will be reduced from 3040 kWh/tonne alloy to 2700 kWh/tonne alloy if pretreatment is implemented. However, the heat exchanger will require an energy input of 535 kWh/tonne alloy, meaning that the extent of energy saved depends on how this energy is generated. For this particular pretreatment, there will not be a decrease in coke consumption in the overall process since the extent of the Boudouard reaction will not be decreased. Nonetheless, given the emission factors for electricity in South Africa, a reduction of 11.2% in indirect CO₂ emissions can be obtained (given that the energy required in the heat exchanger comes from sustainable sources, such as thermal solar).

3.3. Life Cycle Assessment

The simulation results obtained in HSC Sim for all four cases were exported to openLCA, where a life cycle analysis was performed.

Table 8. Results from LCA for different impact categories per tonne of alloy produced for closed furnace case.

Impact Category	Reference Unit	Ecoinvent	SAF	Pretreatment + SAF
Climate change—GWP100	kg CO2-eq	2.661	1.394	1.142
Freshwater eutrophication—FEP	kg P-Eq	0.001	0.0001	0.0000
Photochemical oxidant formation—POFF	kg NMVOC	0.015	0.0034	0.0030
Water depletion—WDP	m3	0.013	0.0534	0.0520

and Figure 5 show the results for the closed furnace simulated case, whereas

Table 9. Results from LCA for different impact categories per tonne alloy produced for open furnace case.

Impact Category	Reference Unit	Ecoinvent	SAF	Pretreatment + SAF
Climate change—GWP100	kg CO2-eq	2.661	1.922	1.915
Freshwater eutrophication—FEP	kg P-Eq	0.001	0.000	0.000
Photochemical oxidant formation—POFF	kg NMVOC	0.015	0.004	0.004
Water depletion—WDP	m3	0.013	0.089	0.079

and Figure 6 show results for the open furnace. The values presented in the column marked “Ecoinvent” are the values obtained from the Ecoinvent database valid for 1 tonne of ferromanganese alloy, whereas columns two (SAF) and three (pretreatment + SAF) present results from LCA obtained for the simulated cases. The values in Table 8 and Table 9 are all based on Norwegian emission factors for electricity.

The main impact factor of interest is the GWP100, which is greenhouse gas emissions quantified as kg CO₂ equivalents per ton of ferromanganese alloy produced. The results show that CO₂ emissions vary largely with production mode/raw materials and, furthermore, largely with the geographical location of the plant. Variations with geographical location correlate to the different emission factors of electricity across the globe. Electricity produced from renewable energy leads to low emissions factors, whereas electricity produced to a larger extent from fossil sources increases the emission factors. Geographical locations may further influence CO₂ emissions by affecting the raw materials used in production, which is commonly driven by cost and availability. Furthermore, it can be seen that there is a large difference between the values found in the Ecoinvent database and the values obtained when a representative simulation model is developed, indicating that relying solemnly on the database values may not provide representative life cycle assessments.

4. Conclusions

A simulation model for the production process of manganese ferroalloys was developed using HSC Sim, enabling the calculation of environmental impact based on a wide range of input parameters. Given the current scarcity and ambiguity of environmental data for ferromanganese production, this model significantly enhances existing databases by providing detailed, specific data linked to well-defined process conditions.

The model accommodates variations in raw materials and production methods, accurately representing the prereduction behavior of different charge mixtures. Two primary production scenarios were evaluated: one involving a closed ferromanganese furnace operating on the duplex method, which achieved a 17.5% reduction in energy consumption and a 16.2% decrease in direct CO₂ emissions through CO-rich off-gas pretreatment. The other was an open furnace employing a discard slag approach that demonstrated an 11.2% reduction in energy consumption with thermal solar energy pretreatment, although no decrease in direct CO₂ emissions was obtained with pretreatment.

The HSC simulation model, initially developed in PreMa, has undergone and will continue to receive enhancements in KSP BioMet (2022–2027). This collaborative project involves Norwegian producers of silicon and ferroalloys—Eramet, Elkem, Wacker, and Finnfjord—along with research partners SINTEF and NTNU. This ongoing development aims to evolve the model by integrating additional parameters, including both variables and process factors, ultimately enhancing the simulation’s capabilities and accuracy.